A greedy training algorithm for sparse least-squares support vector machines

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2415 LNCS, Issue , 2002, Pages 681-686

  Cawley, Gavin C ^a   Talbot, Nicola L C ^a  

^a UNIVERSITY OF EAST ANGLIA   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; ITERATIVE METHODS; NEURAL NETWORKS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; ALGORITHMS; LEAST SQUARES APPROXIMATIONS;

GREEDY ALGORITHMS; KERNEL EXPANSION; KERNEL RIDGE REGRESSIONS; LEAST SQUARES SUPPORT VECTOR MACHINES; PRUNING TECHNIQUES; SPARSE APPROXIMATIONS; TRAINING ALGORITHMS; TRAINING PATTERNS;

LEAST SQUARES APPROXIMATIONS; SUPPORT VECTOR MACHINES;

EID: 84902163067     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-46084-5_111     Document Type: Conference Paper

References (9)

1. J. A. K. Suykens, J. De Brabanter, L. Lukas, and J. Vandewalle. Weighted least squares support vector machines : robustness and sparse approximation. Neurocomputing, 2001.
2. A. E. Hoerl and R. W. Kennard. Ridge regression: Biased estimation for nonorthogonal Problems. Technometrics, 12(1):55-67, 1970.
3. A. N. Tikhonov and V. Y. Arsenin. Solutions of ill-posed Problems. John Wiley, New York, 1977.
4. S. Geman, E. Bienenstock, and R. Doursat. Neural networks and the bias/variance dilemma. Neural Computation, 4(1):1-58, 1992.
5. C. Saunders, A. Gammerman, and V. Vovk. Ridge regression learning algorithm in dual variables. In Proceedings, 15th International Conference on Machine Learning, pages 515-521, Madison, WI, July 24-271998.
6. J. Suykens, L. Lukas, and J. Vandewalle. Sparse approximation using least-squares support vector machines. In Proceedings, IEEE International Symposium on Circuits and Systems, pages 11757-11760, Geneva, Switzerland, May 2000.
7. G. S. Kimeldorf and G. Wahba. Some results on Tchebycheffian spline functions. J. Math. Anal. Applic., 33:82-95, 1971.
8. B. W. Silverman. Some aspects of the spline smoothing approach to non-parametric regression curve fitting. Journal of the Royal Statistical Society, B, 47(1):1-52, 1985.
9. D. Harrison and D. L. Rubinfeld. Hedonic prices and the demand for clean air. Journal Environmental Economics and Management, 5:81-102, 1978.